1. Field of the Invention
The present invention relates to computer-aided diagnosis techniques for automated detection of abnormalities in chest images and relates to U.S. Pat. Nos. 4,851,984, 4,907,156, 4,839,807, 4,918,534, 5,072,384, 5,133,020, 5,289,374, 5,319,549 and 5,343,390 as well as pending U.S. application Ser. Nos. 08/174,175, 08/053,345, 07/915,631, 07/981,471, 08/060,531, 08/235,530, 08/159,133, 08/159,136, 08/158,389, and 08/220,917.
2. Discussion of Background
The aim of computer-aided diagnosis (CAD) is to alert the radiologist by indicating potential lesions and/or providing quantitative information as a second opinion. Since the middle of 1980 a number of computerized schemes for CAD have been under development for chest radiography, mammography, angiography, and bone radiography. In chest radiography, several computerized schemes have been applied to the detection and classification of pneumoconioses and other interstitial diseases, the detection of lung nodules, heart size measurement, and the detection of pneumothorax (see U.S. application Ser. No. 08/174,175). However, development of CAD for chest radiography is still at an early stage. Therefore, it is necessary to further improve the performance based on an understanding of image features of normal and abnormal patterns appearing on radiographs.
It is known that diagnosis of interstitial infiltrates in chest radiographs is one of the most difficult problems in diagnostic radiology because of the complexity and the variety of abnormal patterns due to various diseases. Therefore, interpretations by different radiologists may differ because of the subjective criteria used. Even if the same radiologist interprets the same case on different days, different interpretations may result, particularly in the case of subtle abnormalities. If a computerized scheme could provide quantitative information regarding lung texture, the subjectivity of the interpretation would be decreased, thus improving the accuracy of diagnosis.
Since 1987, Katsuragawa et al (see Med Phys, Vol. 15 (34), pp 311-319, 1988; Med Phys, Vol. 16(1), pp 38-44, 1989; RadioGraphics, Vol. 10, pp 257-269, 1990; and Med Phys, Vol. 20, pp 975-982, 1993) have been developing a computerized scheme for the detection and characterization of interstitial diseases based on analysis of two texture measures, i.e., the root-mean square (RMS) variation and the first moment of the power spectrum, which correspond to the magnitude and coarseness (or fineness), respectively, of the lung texture. Preliminary results have indicated the potential usefulness of CAD for the detection and classification of interstitial disease. However, the achieved results tend to provide a number of false positive ROIs at high optical densities and also a number of false negative ROIs at low optical densities in digitized chest radiographs. Although the calculated texture measures include not only the lung texture but also noise components associated with the screen-film system and the laser scanner, the effects of these noise components on texture measure have not been well understood. Therefore, a better understanding of the optical-density dependence of the texture measures, due to lung texture and other noise components, is needed.
During the past decade, different computerized methods and techniques have been developed for analysis and detection of various abnormalities in chest radiographs. The ribcage boundary and diaphragm edges of posterior-anterior (PA) chest images provide useful information on the location, shape, and size of lung fields and are required by computer-aided diagnosis (CAD) schemes for automated detection of abnormalities in chest images. The accurate detection of the ribcage boundary and diaphragm edges is very important for these methods to work properly. A number of investigators attempted to develop techniques for automated segmentation of chest images. Although it was relatively simple to characterize lung regions by thresholding chest images according to the gray level histogram, this approach was not useful for the development of CAD schemes due to the low accuracy thus obtained in detecting lung boundaries and complete loss of mediastinal areas. McNitt-Gray et al (SPIE Image Processing 1993; 1988: 160-170) developed a pattern classification technique for automated segmentation of chest images. In their study, 33 normal chest images (17 cases for training and 16 cases for testing) were used. The overall accuracy of their method was about 76%.
Another method for the detection of the ribcage boundary which was initially developed by Powell et al (Med. Phys. 1988; 15: 581-587) and later refined by Nakamori et al (Med. Phys. 1990; 17: 342-350; U.S. Pat. No. 5,072,384) was based on the edge detection from the second derivatives of profiles. With these techniques, one can detect ribcage edges only below the clavicle. The complete ribcage boundary was estimated by polynomial curve fitting of detected right and left ribcage edges. Thus, a part of the ribcage boundary in the top lung area was often incorrect. With this method, ribcage edges were determined by the positions yielding minimum values of the second derivative of horizontal profiles in short segments selected over the right and left ribcages. Since there may be several edge patterns in a profile, the minimum peaks in second derivatives might not result from ribcage edges. This problem can become severe if the image contrast is very low near ribcage areas. Therefore, the use of minimum peak positions in second derivatives was found to have an overall accuracy of about 75% and not to be reliable for the correct detection of ribcage edges.
Further, the detection of diaphragm edges of a chest image is difficult and often inaccurate because of the presence of the complicated patterns of stomach gas structures and the effects of cardiac edges around the left hemidiaphragm area. The stomach gas patterns are irregular in shape, and usually are located close to left hemidiaphragm edges. Furthermore, the optical densities of the stomach gas patterns are similar to those of lung areas. Therefore, simple edge gradient analysis methods are unlikely to work well to detect the left hemidiaphragm edges. An accurate method is needed to detect left hemidiaphragm edges in order to improve the overall accuracy of the delineation of the lung fields of chest images.